Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-c...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-05, Vol.18 (6), p.1686
Main Authors: Jung, Jongtack, Lee, Woonghee, Kim, Hwangnam
Format: Article
Language:English
Subjects:
Audio data
audio data processing
Completion time
Computation
Computer simulation
cooperative computing
heavy computation
Internet of Things
low-latency processing
Nodes
Remote sensors
Sensors
Wireless sensor networks
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Summary:Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today's devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, , a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed and carried out the practical implementation on real devices. We confirmed the effectiveness of through various experiments using the real devices and simulations. Using , nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18061686